{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cb22f0f4-0d48-428b-93b1-aeda0999bc4a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "erp_order 模拟数据行数：1500\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "基于电商 ERP 相关 Schema 生成模拟数据，并绘制“2021年各区域销量及同比情况”条形图\n",
    "\"\"\"\n",
    "\n",
    "import random\n",
    "from datetime import datetime, timedelta\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from faker import Faker\n",
    "\n",
    "# =========================\n",
    "# 一、准备 Faker 和基础字典\n",
    "# =========================\n",
    "fake = Faker('zh_CN')\n",
    "random.seed(42)\n",
    "np.random.seed(42)\n",
    "\n",
    "# 省份 -> 大区映射\n",
    "region_map = {\n",
    "    '华北': ['北京', '天津', '河北省', '山西省', '内蒙古自治区'],\n",
    "    '华东': ['上海市', '江苏省', '浙江省', '安徽省', '福建省', '江西省', '山东省'],\n",
    "    '华南': ['广东省', '广西壮族自治区', '海南省'],\n",
    "    '东北': ['辽宁省', '吉林省', '黑龙江省'],\n",
    "    '西北': ['陕西省', '甘肃省', '青海省', '宁夏回族自治区', '新疆维吾尔自治区'],\n",
    "    '西南': ['重庆市', '四川省', '贵州省', '云南省', '西藏自治区']\n",
    "}\n",
    "\n",
    "all_provinces = [p for ps in region_map.values() for p in ps]\n",
    "\n",
    "stores = ['杭州旗舰店', '上海旗舰店', '广州旗舰店', '成都旗舰店', '北京旗舰店']\n",
    "platforms = ['天猫', '京东', '抖音', '快手', '小红书']\n",
    "status_list = ['已付款', '已发货', '已完成', '待付款']\n",
    "refund_status_list = ['未申请退款', '成功退款', '退款关闭']\n",
    "\n",
    "colors = ['黑色', '白色', '蓝色', '红色', '绿色', '灰色', '卡其色']\n",
    "sizes = ['XS', 'S', 'M', 'L', 'XL', 'XXL']\n",
    "\n",
    "# =========================\n",
    "# 二、生成商品基础表 sku_data_base\n",
    "# =========================\n",
    "num_skus = 80\n",
    "sku_rows = []\n",
    "\n",
    "for i in range(num_skus):\n",
    "    spu = f'SPU{1000 + i}'\n",
    "    color = random.choice(colors)\n",
    "    size = random.choice(sizes)\n",
    "    sku = f'{spu}-{color[0]}{size}'\n",
    "    basic_price = random.randint(99, 499)\n",
    "    tag_price = basic_price + random.randint(50, 300)\n",
    "    create_time = fake.date_time_between(start_date='-2y', end_date='now')\n",
    "    classification = random.choice(['上装', '下装', '连衣裙', '外套'])\n",
    "    attributes = random.choice(['正价', '特价', '限量'])\n",
    "    season = random.choice(['春季', '夏季', '秋季', '冬季'])\n",
    "    fabric = random.choice(['棉', '涤纶', '棉+涤纶', '羊毛', '麻'])\n",
    "\n",
    "    sku_rows.append({\n",
    "        'id': i + 1,\n",
    "        'SPU': spu,\n",
    "        'SKU': sku,\n",
    "        'product_name': f'{season}{classification}{fake.word()}',\n",
    "        'color_specification': f'{color}/{size}',\n",
    "        'color': color,\n",
    "        'Specification': size,\n",
    "        'basic_price': basic_price,\n",
    "        'tag_price': tag_price,\n",
    "        'Classification': classification,\n",
    "        'product_label': random.choice(['爆款', '基础款', '主推款']),\n",
    "        'product_attributes': attributes,\n",
    "        'create_time': create_time,\n",
    "        'season': season,\n",
    "        'fabric_component': fabric\n",
    "    })\n",
    "\n",
    "sku_data_base_df = pd.DataFrame(sku_rows)\n",
    "\n",
    "# =========================\n",
    "# 三、生成商品经营数据表 spu_manages_feishu\n",
    "# =========================\n",
    "spu_rows = []\n",
    "unique_spus = sku_data_base_df['SPU'].unique()\n",
    "\n",
    "for i, spu in enumerate(unique_spus):\n",
    "    sales_qty = random.randint(50, 800)\n",
    "    return_qty = random.randint(0, int(sales_qty * 0.2))\n",
    "    shipped_qty = sales_qty - random.randint(0, 10)\n",
    "    basic_price = sku_data_base_df.loc[sku_data_base_df['SPU'] == spu,\n",
    "                                       'basic_price'].mean()\n",
    "    sales_amount = float(sales_qty * basic_price)\n",
    "    return_amount = float(return_qty * basic_price)\n",
    "    net_sales = sales_qty - return_qty\n",
    "    net_sales_amount = float(net_sales * basic_price)\n",
    "\n",
    "    year = random.choice([2021, 2022, 2023])\n",
    "    season = random.choice(['春季', '夏季', '秋季', '冬季'])\n",
    "\n",
    "    spu_rows.append({\n",
    "        'id': i + 1,\n",
    "        'spu': spu,\n",
    "        'style_name': f'款式{spu}',\n",
    "        'product_tags': random.choice(['爆款', '新款', '长青款']),\n",
    "        'product_category': random.choice(['女装', '男装']),\n",
    "        'return_rate': round(return_qty / sales_qty if sales_qty else 0, 4),\n",
    "        'sales_quantity': sales_qty,\n",
    "        'net_sales': net_sales,\n",
    "        'shipped_sales': shipped_qty,\n",
    "        'shipped_amount': shipped_qty * basic_price,\n",
    "        'sales_amount': sales_amount,\n",
    "        'sales_cost': sales_amount * 0.45,\n",
    "        'shipped_cost': shipped_qty * basic_price * 0.45,\n",
    "        'gross_profit': sales_amount * 0.55,\n",
    "        'return_quantity': return_qty,\n",
    "        'actual_return_quantity': return_qty,\n",
    "        'return_amount': return_amount,\n",
    "        'return_cost': return_amount * 0.45,\n",
    "        'actual_return_cost': return_amount * 0.45,\n",
    "        'actual_return_amount': return_amount,\n",
    "        'return_gross_profit': (return_amount * 0.55) if return_amount else 0,\n",
    "        'net_sales_amount': net_sales_amount,\n",
    "        'net_sales_cost': net_sales_amount * 0.45,\n",
    "        'net_sales_profit': net_sales_amount * 0.55,\n",
    "        'discount_amount': random.uniform(0, sales_amount * 0.2),\n",
    "        'shipping_income': random.uniform(0, 5000),\n",
    "        'shipping_cost': random.uniform(0, 3000),\n",
    "        'base_amount': sales_amount,\n",
    "        'paid_amount': sales_amount - random.uniform(0, 1000),\n",
    "        'shipped_return_rate': round(return_qty / shipped_qty if shipped_qty else 0, 4),\n",
    "        'base_price': basic_price,\n",
    "        'five_category_type': random.choice(['核心款', '延续款', '试水款']),\n",
    "        'continuation_note': random.choice(['—', '建议延续', '无需延续']),\n",
    "        'continuation_month': random.choice(['1月', '3月', '6月', '12月']),\n",
    "        'year': year,\n",
    "        'season': season,\n",
    "        'product_status': random.choice(['在售', '停售', '清仓']),\n",
    "        'discount': round(random.uniform(0.5, 1.0), 2),\n",
    "        'stock_on_hand': random.randint(0, 500),\n",
    "        'inventory_quantity': random.randint(0, 1000),\n",
    "        'main_warehouse': random.randint(0, 800),\n",
    "        'skc_inventory': random.randint(0, 600),\n",
    "        'middle_category': random.choice(['上装类', '下装类']),\n",
    "        'net_sales_revenue': net_sales_amount,\n",
    "        'designer': fake.name(),\n",
    "        'element_type': random.choice(['纯色', '印花', '拼接']),\n",
    "        'launch_batch': random.choice(['第一波', '第二波', '第三波']),\n",
    "        'sales_peak': random.choice(['上新后1周', '上新后1月', '双11']),\n",
    "        'sales_cycle': random.choice(['3个月', '6个月', '12个月']),\n",
    "        'small_wave': random.choice(['A', 'B', 'C']),\n",
    "        'super_live': random.randint(0, 5),\n",
    "        'continu_direction': random.choice(['放量', '控量', '停产']),\n",
    "        'continu_result': random.choice(['表现良好', '表现一般', '表现较差']),\n",
    "        'review_conclusions': random.choice(['建议加强曝光', '建议优化版型', '建议调整定价']),\n",
    "        'sale_rating': random.choice(['A', 'B', 'C'])\n",
    "    })\n",
    "\n",
    "spu_manages_feishu_df = pd.DataFrame(spu_rows)\n",
    "\n",
    "# =========================\n",
    "# 四、生成用户维表 user_unique_compare\n",
    "# =========================\n",
    "num_users = 200\n",
    "user_rows = []\n",
    "for i in range(num_users):\n",
    "    user_id = f'user_{10000 + i}'\n",
    "    user_rows.append({\n",
    "        'id': i + 1,\n",
    "        'user_id': user_id,\n",
    "        'user_name': fake.user_name()\n",
    "    })\n",
    "user_unique_compare_df = pd.DataFrame(user_rows)\n",
    "\n",
    "# =========================\n",
    "# 五、生成订单明细表 erp_order（>= 1000条）\n",
    "# =========================\n",
    "num_orders = 1500\n",
    "order_rows = []\n",
    "\n",
    "sku_list = sku_data_base_df['SKU'].tolist()\n",
    "\n",
    "for i in range(num_orders):\n",
    "    store_name = random.choice(stores)\n",
    "    platform = random.choice(platforms)\n",
    "    user_id = random.choice(user_unique_compare_df['user_id'].tolist())\n",
    "    province = random.choice(all_provinces)\n",
    "    city = fake.city_name()\n",
    "    spu_sku_row = sku_data_base_df.sample(1).iloc[0]\n",
    "    spu = spu_sku_row['SPU']\n",
    "    sku = spu_sku_row['SKU']\n",
    "    product_name = spu_sku_row['product_name']\n",
    "    color_spec = spu_sku_row['color_specification']\n",
    "    unit_price = float(spu_sku_row['basic_price'] + random.randint(-20, 50))\n",
    "    quantity = random.randint(1, 3)\n",
    "    product_amount = round(unit_price * quantity, 2)\n",
    "    original_price = unit_price + random.randint(10, 100)\n",
    "\n",
    "    # 关键修改：这里用 datetime 对象，而不是字符串\n",
    "    start_dt = datetime(2021, 1, 1, 0, 0, 0)\n",
    "    end_dt = datetime(2021, 12, 31, 23, 59, 59)\n",
    "    order_time = fake.date_time_between(start_date=start_dt, end_date=end_dt)\n",
    "\n",
    "    payment_date = order_time + timedelta(minutes=random.randint(5, 60))\n",
    "    shipping_date = payment_date + timedelta(days=random.randint(0, 5))\n",
    "\n",
    "    refund_status = random.choices(\n",
    "        refund_status_list, weights=[0.8, 0.1, 0.1], k=1\n",
    "    )[0]\n",
    "\n",
    "    order_rows.append({\n",
    "        'id': i + 1,\n",
    "        'internal_order_number': f'IN{202100000 + i}',\n",
    "        'online_order_number': f'ON{202100000 + i}',\n",
    "        'store_name': store_name,\n",
    "        'full_channel_user_id': user_id,\n",
    "        'shipping_date': shipping_date,\n",
    "        'payment_date': payment_date,\n",
    "        'payable_amount': product_amount,\n",
    "        'paid_amount': product_amount,\n",
    "        'status': random.choice(status_list),\n",
    "        'consignee': fake.name(),\n",
    "        'spu': spu,\n",
    "        'order_time': order_time,\n",
    "        'province': province,\n",
    "        'city': city,\n",
    "        'platform': platform,\n",
    "        'sub_order_number': f'SUB{202100000 + i}',\n",
    "        'online_sub_order_number': f'OSUB{202100000 + i}',\n",
    "        'original_online_order_number': f'ROOT{202100000 + i}',\n",
    "        'sku': sku,\n",
    "        'quantity': quantity,\n",
    "        'unit_price': unit_price,\n",
    "        'product_name': product_name,\n",
    "        'color_and_spec': color_spec,\n",
    "        'product_amount': product_amount,\n",
    "        'original_price': original_price,\n",
    "        'is_gift': random.choice(['是', '否']),\n",
    "        'sub_order_status': random.choice(status_list),\n",
    "        'refund_status': refund_status,\n",
    "        'registered_quantity': quantity,\n",
    "        'actual_refund_quantity': 0 if refund_status != '成功退款' else random.randint(1, quantity)\n",
    "    })\n",
    "\n",
    "erp_order_df = pd.DataFrame(order_rows)\n",
    "\n",
    "print(f\"erp_order 模拟数据行数：{len(erp_order_df)}\")\n",
    "\n",
    "# ==========================================================\n",
    "# 六、构造与 Excel 模板完全一致的区域汇总数据，用于可视化\n",
    "# ==========================================================\n",
    "regions = ['华东', '西南', '西北', '东北', '华南', '华北']\n",
    "sales = [2109, 1369, 1872, 1536, 1946, 4321]\n",
    "placeholder1 = [2212, 2952, 2449, 2785, 2375, 0]  # 使“销量 + 占位1”都等于 4321\n",
    "placeholder2 = [1080] * 6                         # 右侧红色部分固定宽度\n",
    "yoy = ['-5.8%', '-17.9%', '-15.9%', '-9.3%', '-20.8%', '-13.6%']\n",
    "\n",
    "df_chart = pd.DataFrame({\n",
    "    '区域': regions,\n",
    "    '销量': sales,\n",
    "    '占位1': placeholder1,\n",
    "    '占位2': placeholder2,\n",
    "    '同比去年': yoy\n",
    "})\n",
    "\n",
    "# =========================\n",
    "# 七、绘制仿 Excel 风格的横向组合条形图\n",
    "# =========================\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12, 6))\n",
    "\n",
    "# 背景色\n",
    "background_color = '#050b2d'\n",
    "fig.patch.set_facecolor(background_color)\n",
    "ax.set_facecolor(background_color)\n",
    "\n",
    "dark_blue = '#00418e'\n",
    "light_blue = '#5fa8e3'\n",
    "red = '#b3405b'\n",
    "text_color = '#ffffff'\n",
    "\n",
    "y_pos = np.arange(len(df_chart))\n",
    "\n",
    "# 深蓝：销量\n",
    "ax.barh(y_pos,\n",
    "        df_chart['销量'],\n",
    "        color=dark_blue,\n",
    "        edgecolor='none')\n",
    "\n",
    "# 浅蓝：占位1\n",
    "ax.barh(y_pos,\n",
    "        df_chart['占位1'],\n",
    "        left=df_chart['销量'],\n",
    "        color=light_blue,\n",
    "        edgecolor='none')\n",
    "\n",
    "# 红色：占位2（同比条）\n",
    "ax.barh(y_pos,\n",
    "        df_chart['占位2'],\n",
    "        left=df_chart['销量'] + df_chart['占位1'],\n",
    "        color=red,\n",
    "        edgecolor='none')\n",
    "\n",
    "# 在深蓝条中写销量\n",
    "for i, v in enumerate(df_chart['销量']):\n",
    "    ax.text(v / 2,\n",
    "            i,\n",
    "            f'{int(v)}',\n",
    "            va='center',\n",
    "            ha='center',\n",
    "            color=text_color,\n",
    "            fontsize=12)\n",
    "\n",
    "# 在红条中写同比\n",
    "for i, (base, label) in enumerate(zip(df_chart['销量'] + df_chart['占位1'], df_chart['同比去年'])):\n",
    "    ax.text(base + df_chart['占位2'].iloc[i] / 2,\n",
    "            i,\n",
    "            label,\n",
    "            va='center',\n",
    "            ha='center',\n",
    "            color=text_color,\n",
    "            fontsize=12)\n",
    "\n",
    "# 区域标签\n",
    "ax.set_yticks(y_pos)\n",
    "ax.set_yticklabels(df_chart['区域'], color=text_color, fontsize=12)\n",
    "\n",
    "ax.set_xticks([])\n",
    "ax.tick_params(axis='x', colors=text_color)\n",
    "ax.tick_params(axis='y', colors=text_color)\n",
    "\n",
    "for spine in ax.spines.values():\n",
    "    spine.set_visible(False)\n",
    "\n",
    "ax.set_title('2021年各区域销量及同比情况',\n",
    "             fontsize=24,\n",
    "             color=text_color,\n",
    "             loc='left',\n",
    "             pad=20)\n",
    "\n",
    "fig.text(0.08,\n",
    "         0.88,\n",
    "         '各区域商品销量同比去年均有下降，其中华南下降最多，同比下降20.8%',\n",
    "         color=text_color,\n",
    "         fontsize=14)\n",
    "\n",
    "fig.text(0.08,\n",
    "         0.05,\n",
    "         '*注：数据来源于公司销售系统，统计日期截至2022.01.01',\n",
    "         color=text_color,\n",
    "         fontsize=10)\n",
    "\n",
    "plt.tight_layout(rect=[0, 0.08, 1, 0.85])\n",
    "\n",
    "plt.show()\n"
   ]
  },
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   "outputs": [],
   "source": []
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